The present application relates generally to the control of industrial machines and assets. More specifically, but not by way of limitation, the present application relates to controlling industrial machines and assets, particularly those related to power generation industry, via controllers implementing analytics, processes, and graphical screen displays that improve aspects of operational performance.
For example, in regard to the power generation industry, the marketplace typically includes geographically defined power systems within which several competing producers or power plants generate electricity that is distributed over common transmission lines for delivery to customers. Each of the power plants may include several generating units that enable many different generating configurations and possible output levels. While there are many types of generating units, it will be appreciated that thermal generating units—such as gas turbines, steam turbines, and combined-cycle plants—are still prevalently relied on to generate a significant portion of the electrical power that customers require. Power systems typically also include a central governing authority, often referred to as a dispatch authority, that establishes and administers a competitive dispatch process that determines how the anticipated customer load for a future generating period will be divided among and generated by the participant power plants.
As part of the dispatch process, the managers or operators of power plants produce offer curves for submittal to the dispatch authority. Such offer curves represent bids by the power plants and, typically, communicate the anticipated generating cost for the power plant during the upcoming generating period, for example, indicating an incremental variable cost curve or some other suitable indication of variable generating expense. The dispatch authority then analyzes the submitted offer curves to determine the level at which to engage each power plant that most effectively satisfy the predicted load demand of the customers. In doing this, the dispatch authority may consider many factors, including the reliability of the different power plants, with a primary goal being to utilize the available power plants in a way that achieves the lowest generating cost for the customers. Once this is done, the dispatch authority produces a commitment schedule for the power plants that describes with specificity the extent to which each will be engaged in the upcoming generating period.
To operate successfully within this type of competitive environment, participant power plants must be able to achieve high levels of operational efficiency and cost-effectiveness. Additionally, plant operators must be able to call upon accurate, real-time data informing them of the current performance characteristic of their power generating machinery. This gives operators the ability to bid their power plant's actual capabilities without having to include large error margins in the offer curves they submit to the dispatch authority, which allows their bids to be as competitive as possible. Without such knowledge, such error margins typically needed to reduce the risk of overbidding the power plant during the dispatch process, which can be a costly mistake for the power plant because it may force the plant to operate inefficiently to satisfy the commitment schedule. If, however, those error margins can be reduced or minimized without increasing such risks by providing operators with more accurate and timely intelligence regarding the actual performance capabilities of their plants, this reduction translates directly into making their dispatch bids more competitive. As will be seen, one or more aspects of the present application may be employed toward improving power plant efficiency and cost-effectiveness and/or enhancing operator intelligence around the true generating capabilities of their power plants.
Once the commitment schedule is communicated to the power plants, the objective of each is to generate the committed output in a manner that maximizes economic return. As will be appreciated, given the growing complexity of the modern power plant, this objective is becoming particularly challenging. This complexity is the product of many factors. For example, power plants now typically include many different generating units of varying types, and these enable numerous alternative generating configurations, with each of these alternatives attending its own set of economic considerations. Each of these generating units also must be maintained according to its own maintenance schedule that requires regular outages that must be carefully planned so to not unnecessarily impact plant operations. In addition, the power industry is a heavily regular one, with numerous laws and regulations affecting how power plants can be operated. And, of course, fluctuating market conditions make short- and long-term profitability a moving target. While modern power plants typical include both unit-level and plant-level control systems for addressing some of these operational issues, in many ways, these control systems are inadequate because they do not fully leverage the growing data-intensive aspects of the industrial world. Specifically, current control systems have been unsuccessful at realizing the level of industrial optimization that the growing availability of operational data make possible, and this failure results in power plants and generating units being operated inefficiently and without maximizing economic return. As will be seen, one or more aspects of the present application address these operational shortcomings.
More generally, as digital and industrial environments become more fully integrated and data-intensive, many of these issues are similarly applicable to other types of industrial plants. As with power plants, other large industrial assets or plants have become increasing complex, yet slow to fully marshal the available technology toward achieving the gains in efficiency that are possible. Most of today's industrial plants are assembled from many disparate types of industrial machines, control systems, and other assets, which are supplied by a myriad of different manufacturers. While each of these different components may be adequately designed to perform particular tasks, the overall success of the plant depends upon them being seamlessly integrated so that their combined function is both efficient and highly productivity. This presents very difficult design and operational challenges.
A long-preferred solution for driving the operation of such industrial plants has been integrated software and hardware-based controllers. However, with the rise of inexpensive cloud computing, increasing sensor capabilities and decreasing sensor costs, as well as the proliferation of mobile technologies and networking capabilities, new possibilities have arisen to reshape how industrial plants and other assets are designed, operated, and maintained. For example, recent advances in sensor technologies now enable the harvesting of new types and vastly more operational data, while progress in network speed and capacity allows essentially real-time transmission of this data to distant locations. This means, for example, that even for geographically dispersed fleets of similar assets, data gathered at each remote site can be efficiently brought together, analyzed, and employed in ways aimed at improving both fleet, plant, or individual asset performance. As a consequence of this evolving and data-intensive environment, new opportunities arise to enhance or optimize the value of industrial plants and assets through novel industrial-focused hardware and software solutions. Such solutions would have value and utility and are a subject of the present disclosure.